Nonlinear Regression Options
Options allow you to control various aspects of your nonlinear regression analysis:
Bootstrap Estimates. A method of estimating the standard error of a statistic using repeated samples from the original data set. This is done by sampling (with replacement) to get many samples of the same size as the original data set. The nonlinear equation is estimated for each of these samples. The standard error of each parameter estimate is then calculated as the standard deviation of the bootstrapped estimates. Parameter values from the original data are used as starting values for each bootstrap sample. This requires the sequential quadratic programming algorithm.
Estimation Method. Allows you to select an estimation method, if possible. (Certain choices in this or other dialog boxes require the sequential quadratic programming algorithm.) Available alternatives include Sequential quadratic programming and Levenberg-Marquardt.
- Sequential Quadratic Programming. This method is available for constrained and unconstrained models. Sequential quadratic programming is used automatically if you specify a constrained model, a user-defined loss function, or bootstrapping. You can enter new values for Maximum iterations and Step limit, and you can change the selection in the drop-down lists for Optimality tolerance, Function precision, and Infinite step size.
- Levenberg-Marquardt. This is the default algorithm for unconstrained models. The Levenberg-Marquardt method is not available if you specify a constrained model, a user-defined loss function, or bootstrapping. You can enter new values for Maximum iterations, and you can change the selection in the drop-down lists for Sum-of-squares convergence and Parameter convergence.
Specifying Nonlinear Regression Options
This feature requires the Regression option.
- From the menus choose:
- In the Nonlinear Regression dialog box, click Options.